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• Within-subject design is the study design where two or more measures are obtained from a sample of subjects.
• Mixed design is a factorial design that combines within-subjects and between subjects factors.
• Within-subjects design. One of the most significant benefits of this type of experimental design is that is does not require large pool of participants.
• A within-subject design can also help reduce errors associated with individual differences
• The sheer act of having participants take part in one condition can impact the performance or behavior on all the other conditions or the carryover effect.
• Fatigue effect is the changes in performance caused by fatigue, boredom, or irritation.
• Performance on subsequent test can also be affected by practice effect.
• It is the chance of detecting a genuine effect of the independent variable known as power.
• It is the changes in subject's response that are caused by testing in multiple treatment conditions; includes order effects, such as the effects of practice or fatigue, which is known as the progressive error.
• It is a technique for controlling order effects by distributing progressive error across the different treatment conditions of the experiment; may also control carryover effects that is called counterbalancing.
• Subject-by-subject counterbalancing is a technique for controlling progressive error for each individual subjects by presenting all treatment conditions more than once.
• Reverse Counterbalancing is a technique for controlling error for each individual subjects by presenting all treatment conditions twice, first in order, then reverse order.
• Block Randomization is a process of randomization that first creates treatment blocks containing one random order of the conditions in the experiment; subjects are then assigned to fill each successive treatment block
• Across-Subject counterbalancing is for controlling progressive error that pools all subjects' data together to equalize the effects of progressive error for each of the condition.
• Complete counterbalancing is using all possible sequences that can be formed out of the treatment conditions and using each sequence the same number of times. For instance, 6 groups in 6 sequences.
• Randomized partial counterbalancing involves selecting a subset at random and is one of the simplest partial counterbalancing procedure in which the randomly select as many sequences of treatment conditions are there are subjects for the experiment.
• Latin square counterbalancing is a particularly effective procedure for controlling order effects in which a matrix or square, of sequences is constructed so that each treatment appears only in any order position.
• Small N is used to study the behavior of only one or few subjects is which requires a very careful control over conditions of the experiment.
• ABA is the order of the treatment conditions
• Baseline is a measure of behavior as it normally occurs without experimental manipulation; a control condition used to assess the impact of the experimental condition.
• AB design in which (A) is the baseline condition is measured first, followed by measurements during the experimental intervention (B); there is no return to the baseline condition.
• ABA design. In which baseline condition is measured first, followed by measurements during experimental condition, then followed by baseline condition to verify that the change in behavior is linked to the experimental conditions; also called as reversal design.
• ABAB design ---baseline->treatment->baseline->treatment.
• ABABA design--baseline->treatment->baseline->treatment->baseline is the final baseline measurement condition to verify that the changes in behavior is linked to the experimental condition.
• Multiple baseline design is very useful for testing a treatment on several behaviors or in different settings and for testing the same treatment in a few different individuals.
• Changing Criterion design is used to modify behavior when the behavior cannot be changed all at once; instead, the behavior is modified in increments, and the criterion for success changes the behavior is modified.
• Discrete trials design is a design that relies on presenting and averaging across many, many experimental trials; repeated applications result in a reliable picture of the effects of the independent variable.
• Large N design is a design in which the behavior of groups of subjects us compared.
• Multiple baseline design is a small N design in which a series of baseline and treatments are compared; once established, however, a treatment is not withdrawn.
• Chi-square test is a non-parametric, inferential statistic that tests whether the frequencies of responses in our sample represent certain frequencies in the population; used in nominal data and categorical data.
• Chi-square goodness of fit test is used to compare a randomly collected sample containing a single, categorical variable to a larger population. This test is most commonly used to compare a random sample to the population from which it was potentially collected.
• Chi-square test for independence looks for an association between two categorical variables within the same population. It does not compare a single observed variable to a theoretical population.
• Chi-square test for homogeneity compares the proportions of responses from two or more populations with regards to a dichotomous variable (e.g. male/female, yes/no) or variable with more than two outcome categories.
• Critical Value is the value of the test statistic that must be exceeded to reject the null hypothesis at the chosen significance level.
• Degrees of freedom is the number of members of a set of data that can vary or change value without changing the value of a known statistic for those data and it is also used in statistical analysis to indicate how many ways the obtained results could have been found through random sampling.
• A t-test is an inferential statistic used to determine if there is a significant difference between the means of two groups and how they are related.
• One way ANOVA compares the means of two or more independent groups in order to determine whether there is statistical evidence that the associated population means are significantly different
• A one-way repeated measures ANOVA (also known as a within-subjects ANOVA) is used to determine whether three or more group means are different where the participants are the same in each group.
• A two-way ANOVA is used to estimate how the mean of a quantitative variable changes according to the levels of two categorical variables.
• Two way anova repeated measures compares the mean differences between groups that have been split on two within-subjects factors.